Fung Wai-man. / Thesis (M.Phil.)--Chinese University of Hong Kong. / Includes bibliographies.

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Lai, Kin Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 92-97). / Abstracts in English and Chinese. / Title Page --- p.i / Thesis Committee --- p.ii / Abstract (In English) --- p.iv / Abstract (In Chinese) --- p.v / Acknowledgement --- p.vi / List of Figures --- p.xii / List of Tables --- p.xiv / List of Symbols and Abbreviations --- p.xvi / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Chemical Shift Predictions of Nucleic Acids --- p.1 / Chapter 1.1.1 --- Chemical Shift --- p.1 / Chapter 1.1.2 --- Chemical Shift Prediction of Double Helical DNA and RNA --- p.1 / Chapter 1.1.3 --- Chemical Shift Prediction of Random Coil DNA --- p.2 / Chapter 1.1.4 --- Applications of Nucleic Acid Chemical Shift Prediction --- p.4 / Chapter 1.2 --- General Review of DNA Structure --- p.4 / Chapter 1.2.1 --- Structure and Nomenclature of Nucleotide --- p.4 / Chapter 1.2.2 --- Structure of Polynucleotide --- p.5 / Chapter 1.2.3 --- Sugar Conformation in Nucleotide --- p.5 / Chapter 1.2.4 --- Double Helical DNA Conformation --- p.7 / Chapter 1.3 --- A.A Mismatches in DNA Duplexes --- p.8 / Chapter 1.3.1 --- Mismatches in DNA Duplexes --- p.8 / Chapter 1.3.2 --- Biological Significance of A． A Mismatches --- p.9 / Chapter 1.4 --- Purpose of the Work --- p.9 / Chapter 2 --- Materials and Method --- p.10 / Chapter 2.1 --- Overview of the Proposed Prediction Method --- p.10 / Chapter 2.1.1 --- Nearest Neighbor Model --- p.10 / Chapter 2.1.2 --- Base Pair Replacement Approach --- p.10 / Chapter 2.2 --- Sample Design --- p.11 / Chapter 2.2.1 --- Reference Sequences for Obtaining Triplet Values and Correction Factors --- p.11 / Chapter 2.2.2 --- Sequences for Verifying the Base Pair Replacement Approach --- p.12 / Chapter 2.2.3 --- Sequences for Testing Chemical Shift Prediction Accuracy --- p.12 / Chapter 2.3 --- Sample Preparation --- p.13 / Chapter 2.4 --- NMR Experiments --- p.14 / Chapter 2.4.1 --- Non-labile Proton Resonance Assignment --- p.14 / Chapter 2.4.2 --- Labile Proton Resonance Assignment --- p.16 / Chapter 2.5 --- Validating the Assumption in Reference Hairpin Model Samples --- p.17 / Chapter 3 --- Establishment of Proton Chemical Shift Prediction method of A.A Mismatches in B-DNA Duplexes --- p.18 / Chapter 3.1 --- Resonance Assignment --- p.18 / Chapter 3.1.1 --- Non-labile Protons --- p.18 / Chapter 3.1.2 --- Labile Protons --- p.20 / Chapter 3.2 --- Validating the Assumption in Reference Hairpin Model Samples --- p.21 / Chapter 3.3 --- Extraction of A.A Mismatch Triplet Chemical Shift Values --- p.22 / Chapter 3.4 --- Calculation of the 5´ة- and 3´ة-Correction Factors --- p.24 / Chapter 3.5 --- Chemical Shift Prediction Using Triplet Values and Correction Factors Extracted from Top Strands of refA.A(XAY) and refA．T(XAY) --- p.27 / Chapter 3.6 --- Chemical Shift Prediction Using Triplet Values and Correction Factors Extracted from Bottom Strands of refA.A(XAY) and refA．T(XAY) --- p.28 / Chapter 4 --- Testing of Proton Chemical Shift Prediction of A.A Mismatches in B- DNA --- p.29 / Chapter 4.1 --- Prediction Result Using Triplet Values and Correction Factors Extracted from the Top Strands of refA.A(XAY) and refA.T(XAY) --- p.29 / Chapter 4.2 --- Prediction Result Using Triplet Values and Correction Factors Extracted from Bottom Strands of refA.A(XAY) and refAT(XAY) --- p.30 / Chapter 4.3 --- Applicability of the Base Pair Replacement Approach --- p.31 / Chapter 4.3.1 --- Chemical Shifts and 3JH1´ةH2´ة of refT.A(XTY) Sequences --- p.31 / Chapter 4.3.2 --- Correction factors Extracted from the Top Strands of refA.A(XAY) and refT.A(XTY) --- p.31 / Chapter 4.3.3 --- Prediction Result Using Correction Factors Extracted from the Top Strands of refA.A(XAY) and refT.A(XTY) --- p.33 / Chapter 5 --- Conclusion --- p.35 / Appendix I NOE Sequential Assignment of refA.T(XAY) - (A) Aromatic Protons at 25 °C; (B) Labile Protons at 25 °C --- p.36 / Appendix II NOE Sequential Assignment of refA.A(XAY) - (A) Aromatic Protons at 25 °C; (B) Labile Protons at 5 °C --- p.40 / Appendix III H1'-H2'/H2´ح region of DQF-COSY Spectra of refA.T(XAY) at 25 °C --- p.44 / Appendix IV H1'- H2'/H2´ح region of DQF-COSY Spectra of refA.A(XAY) at 25 °C --- p.46 / Appendix V H3' region of HSQC Spectra of refA T(XAY) at 25 °C --- p.48 / Appendix VI H3' region of 1H-31̐ư HSQC Spectra of refA.A(XAY) at 25 °C --- p.50 / Appendix VII 3JH1'h2'1H and 31P Chemical Shifts of refA T(XAY) --- p.52 / Appendix VIII 3JH1'H2'and 31P Chemical Shifts of refA.A(XTY) --- p.60 / Appendix IX NOE Sequential Assignment of refT .A(XTY) - (A) Aromatic Protons at 25 °C; (B) Labile Protons at 25 °C --- p.68 / Appendix X H1'-H2'/H2''region of DQF-COSY Spectra of refT.A(XTY) --- p.72 / Appendix XI H3'region of H-31P HSQC Spectra of refT.A(XTY) --- p.74 / Appendix XII 3JH1'H2'1H and 31P Chemical Shifts of refT.A(XTY) --- p.76 / Appendix XIII Chemical Shifts of Testing Sequences --- p.84 / Reference --- p.92

Magnetic resonance spectroscopy has been widely used in medical applications, rendering precise evaluation and diagnosis in clinics. As the development of various tools for automatic spectra analysis, providing objective quantification of metabolites, absolute concentrations has been playing an important role in clinical studies and applications as well. In this study, we investigate the reliability and accuracy of absolute concentration quantified by LCModel. Ten healthy subjects were included. We compared the resultant concentrations calculated by internal water scaling and phantom calibration, both of which are provided by LCModel. Partial volume effect was also taken into account to improve the accuracy of absolute concentrations. Automatic segmentation was applied to volume of interest in order to separate gray matter and white matter, which will facilitate the further partial volume correction and thus better accuracy of absolute quantification.

Magnetic resonance spectroscopy is now in widespread use, which with various tools of spectra analysis can provide concentrations of metabolites. The influence of metabolites on human physiology is greatly. Due to the tiny variation of the concentration in various metabolites, the analytic method used in the quantitative determination of the absolute concentrations of metabolites plays an important role in this research area. In this thesis we present an analysis tool for segmentation of white matter, gray matte and cerebrospinal fluid using region growing with spatial space, and provide manual interaction for exception handling in this subject. Then we use this tool to analyze different percentages of white matter and gray matter with the default parameter by LCModel and correct partial volume effect. The results show that the proposed tool can improve significantly the accuracy in absolute quantitative analysis of concentration.

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Proton nuclear magnetic resonance (NMR) spectroscopy has been used to investigate the interaction of yeast phosphoglycerate kinase (PGK) with its phosphate containing substrates, ATP and 3-phosphoglycerate (3-PG). The application of one-dimensional and, for the first time, two-dimensional proton NMR techniques to this large protein has enabled specific resonance assignments to be made. Assignment has been aided by the investigation of specifically deuterated protein and site-specific mutant forms of the protein, including the isolated N- and C-domains. The effects of ATP and 3-PG binding on the proton NMR spectrum of yeast PGK have been characterised and the assigned resonances used as local probes of structural and dynamic changes. Two binding sites have been determined for the nucleotide substrate, ATP, the occupancies of which are dependent on Mg²⁺ concentration. One site corresponds to the catalytic site determined crystallographically. A single binding site was found for 3-PG. This binding was shown to cause highly specific conformational changes throughout the N-domain and the interdomain region, which involve the relative movement of at least three α-helices. Investigation of 3-PG binding to several site-specific mutant forms of yeast PGK revealed a critical role for arginine 168 in the propagation of these changes. The general binding of anions to yeast PGK was investigated using the paramagnetic probes [Cr(CN)₆]^3- and [Fe(CN)₆]^3-, and the diamagnetic anion [Co(CN)₆]^3-. The primary anion binding site was determined from [Cr(CN)₆]^3- broadening data and found to share some side-chains involved in 3-PG binding, namely histidine 62 and arginine 168. Evidence for a secondary anion site was found. The anion binding data is discussed in view of the complex activation/inhibition effects of anions on the catalytic activity. Investigation of the isolated N- and C-domains showed that both can fold independently and confirmed that the C-domain is a nucleotide binding domain. It appears that the presence of the interdomain residues and/or the C-terminal peptide are necessary for 3-PG binding to the N-domain. This work shows that the specificity of the substrates is in binding, as expected, but also in the motions induced in the protein as a whole.

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Proton magnetic resonance spectroscopy has been widely applied to the diagnosis of brain diseases. In the meanwhile, the classification of brain abscesses plays an important role on the accurate prognosis in clinics. Recently, the interest in using proton MRS to classify pyogenic brain abscesses has been arising because of its non-invasive property and good accuracy in detecting metabolites. The brain abscess can be classified by means of the metabolites observed in the MR spectra, which may thus benefit the accuracy of the brain abscess diagnosis clinically. However, the interpretation of MR spectra by experienced radiologists can be also very subjective and therefore results in the variation of diagnosis. In this study, we investigate the potential possibility of using Principal Component Analysis (PCA) to classify the short TE MR spectra in more objective way.

Magnetic Resonance Imaging (MRI) can obtain the tissues of in vivo non-invasively. Proton MR Spectroscopy uses the resonance principle to collect the signals of proton and transforms them to spectrums. It provides information of metabolites in patient¡¦s brain for doctors to observe the change of pathology. Observing the metabolites of brain abscess patients is most important process in clinical diagnosis and treatment. Then, doctors use different spectrums of echo time (TE) to enhance the accuracy in the diagnosis. In our study, we use independent component analysis (ICA) to analyze MR spectroscopy. After analyzing, the independent components represent the elements which compose the input data. Then, we use the projection which is mentioned by Ssu-Ying Lu¡¦s Thesis to help us observe the relationship between independent components and spectrums of patients. We also discuss the result of spectrums with using ICA and PCA and discover some questions (whether it need to do scale normalization before inputting data or not, the result of scale normalization doesn¡¦t expect, and the peak in some independent components confuse us by locating in indistinct place) to discuss and to find possible reason after experiments.